centers=dset.centers,
title='Sampled data points',
path='{}/samples.png'.format(exp_dir))
prefix = "unrolled_steps-{}-prior_std-{:.2f}".format(
config.unrolled_steps, np.std(dset.p))
print("Save file with prefix", prefix)
G = Generator(input_size=config.g_inp,
hidden_size=config.g_hid,
output_size=config.g_out).cuda()
G._apply(lambda t: t.detach().checkpoint())
D = Discriminator(input_size=config.d_inp,
hidden_size=config.d_hid,
output_size=config.d_out).cuda()
D._apply(lambda t: t.detach().checkpoint())
criterion = nn.BCELoss(
) # Binary cross entropy: http://pytorch.org/docs/nn.html#bceloss
def binary_cross_entropy(x, y):
loss = -(x.log() * y + (1 - x).log() * (1 - y))
return loss.mean()
criterion = binary_cross_entropy
d_optimizer = optim.Adam(D.parameters(),
lr=config.d_learning_rate,
betas=config.optim_betas)
g_optimizer = optim.Adam(G.parameters(),
lr=config.g_learning_rate,
betas=config.optim_betas)
